Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western Taiwan

Environmental Pollution, Год журнала: 2024, Номер 346, С. 123662 - 123662

Опубликована: Фев. 26, 2024

Язык: Английский

---

Prediction of developmental toxic effects of fine particulate matter (PM2.5) water-soluble components via machine learning through observation of PM2.5 from diverse urban areas

The Science of The Total Environment, Год журнала: 2024, Номер 946, С. 174027 - 174027

Опубликована: Июнь 19, 2024

The global health implications of fine particulate matter (PM

Язык: Английский

---

Meteorology-driven trends in PM2.5 concentrations and related health burden over India

Atmospheric Research, Год журнала: 2024, Номер 308, С. 107548 - 107548

Опубликована: Окт. 1, 2024

Язык: Английский

---

Assessing the Influence of Meteorological Conditions on Pm2.5 Concentrations in Malaysia Using the Wrf Model

Опубликована: Янв. 1, 2025

Язык: Английский

---

Development of a multi-module data-driven integrated framework for identifying drivers of atmospheric particulate nitrate and reduction emissions: An application in an industrial city, China

Environment International, Год журнала: 2025, Номер unknown, С. 109394 - 109394

Опубликована: Март 1, 2025

Atmospheric particulate nitrate (pNO3-), a crucial component of fine matter, significantly contributes to haze pollution. The formation pNO3- is driven by multiple factors including meteorology, emissions, and atmospheric chemistry. Understanding the key drivers developing an accurate physically meaningful method for timely assessment direct causes pollution are essential. In this study, we propose multi-module data-driven integrated framework that incorporates improves four distinct machine learning modules. This enhances physical interpretability statistical outcomes driving pNO3-, quantifies impacts on reveals emission reduction trends. Our findings show meteorology emissions affect 35.3 % 64.7 %, respectively, while chemistry (48.0 %) humidity (17.1 its formation. Photochemistry promotes in summer, whereas liquid-phase reactions dominate winter at higher levels (>60 %). industry source (IS) (14.3 %), combustion (CS) (12.8 transportation (TS) (11.8 main sources. primary transformation NOx emitted from CS TS more sensitive changes meteorological conditions, controlling has greater benefits reduce pNO3-. proposed could provide reliable identifying different events, supporting formulation control measures.

Язык: Английский

---

The spatiotemporal variations of PM2.5 concentration and its relationship with meteorological parameters: A multi-scale analysis in Madrid and Valencia, Spain

Atmospheric Research, Год журнала: 2025, Номер 323, С. 108167 - 108167

Опубликована: Апрель 23, 2025

Язык: Английский

---

Quantifying role of source variations on PM2.5-bound toxic components under climate change: measurement at multiple sites during 2018-2022 in a Chinese megacity

Journal of Hazardous Materials, Год журнала: 2025, Номер 494, С. 138584 - 138584

Опубликована: Май 10, 2025

Язык: Английский

---

Kolmogorov-Zurbenko filter coupled with machine learning to reveal multiple drivers of surface ozone pollution in China from 2015 to 2022

The Science of The Total Environment, Год журнала: 2024, Номер 949, С. 175093 - 175093

Опубликована: Июль 29, 2024

Язык: Английский

---

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122615 - 122615

Опубликована: Сен. 24, 2024

Язык: Английский

---

PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

Discover Artificial Intelligence, Год журнала: 2024, Номер 4(1)

Опубликована: Ноя. 3, 2024

The atmosphere's fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, mortality rate is continuously increasing, requiring immediate inclination scientific community towards design and development advanced predictive models. Conventional statistical approaches become dormant due their limitations in capturing innate relationships between pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine deep learning techniques shown great potential forecasting air quality, providing more accuracy than its predecessor techniques. present study investigates utilization hybrid by integrating models with improve prediction capabilities concentration. It uses datasets from World Air Quality Index (WAQI) State Global (SOGA) analyze performance on both daily annual data, respectively. This ensures model's effectiveness a diversified dataset. implements Random Forest (RF), Polynomial Regression (PR), XGBoost, Extra Tree Regressor (ETR) coupled Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM) obtaining optimized results. Finally, after thorough investigation, PR model FCNN (PR-FCNN) found be best improved R-squared (R2) values, portraying concentration accurately. Based experimentation, preset recommends implementing approaches, offering better especially PM2.5.

Язык: Английский

---